• Part 1; Introducing R
• Part 2; Data Wrangling
• Part 3; Visualizations and a bit statistics

The complete source code for the webinars and all dependent data, and files can be found on Github.com/uashogeschoolutrecht.

1. When things go wrong
2. Why Reproducible (Open) Science?
3. The need for learning programming
4. An example of Reproducible (Open) Science

\(Reproducible\ (Open)\ Science =\) \(Reproducible\ Research + Open\ Science\)

• To assess validity of science and methods we need access to data, methods and conclusions
• To learn from choices other researchers made
• To learn from omissions, mistakes or errors
• To prevent publication bias (also negative results will be available in reproducible research)
• To be able to re-use and/or synthesize data (from many and diverse sources)
• To have access to it all!

Nature Collection on this topic

How would you ‘describe’ the steps of an analysis or creation of a graph when you use GUI* based software?

“You can only do this using code, so it is (basically) impossible in a GUI”

*Graphical User Interface (GUI)…is a form of user interface that allows users to interact with electronic devices through graphical icons and audio indicator such as primary notation, instead of text-based user interfaces, typed command labels or text navigation…

**The file “./Rmd/steps_to_graph_from_excel_file.html” shows you how to do this using the programming language R. In day 3, we will revisit this example.

• Scientific context, research questions and state of the art [P]
• (Experimental) model or characteristics of population or matter studied [P]
• Data that was generated and corresponding meta data [D, C]
• Exact (experimental) design of the study [P, D, C]
• Exploratory data analysis of the data [P, C]
• Exact methods that were used to conduct any formal inference [P, C]
• Model diagnostics [C]
• Interpretations of the (statistical) model results/model fitting process [P, C]
• Conclusions and academic scoping of the results [P, C]
• Access to all of the above [OAcc, OSrc]

\(P = Publication\), \(D = Data\), \(C = Code\), \(OAcc = Open\ Access\), \(OSrc = Open\ Source\)

Assume we have the following question: “Which of 4 types of chairs takes the least effort to arise from when seated in?” We have the following setup:

• 4 different types of chairs
• 9 different subjects (probably somewhat aged)
• Each subject is required to provide a score (from 6 to 20, 6 being very lightly strenuous, 20 being extremely strenuous) when arising from each of the 4 chairs. There is some ‘wash-out’ time in between the trials. The chair order is randomised.

To analyze this experiment statistically, the model would need to include: the rating score as the measured (or dependent) variable, the type of chair as the experimental factor and the subject as the blocking factor

A typical analysis method for this type of randomized block design is a so-called ‘multi-level’ or also called ‘mixed-effects’ or ‘hierarchical’ model. An analysis method much used in clinical or biological scientific practice.

You could also use one-way ANOVA but I will illustrate why this is not a good idea

Wretenberg, Arborelius & Lindberg, 1993

library(nlme)
ergoStool %>% as_tibble()

## # A tibble: 36 × 3
##    effort Type  Subject
##     <dbl> <fct> <ord>  
##  1     12 T1    1      
##  2     15 T2    1      
##  3     12 T3    1      
##  4     10 T4    1      
##  5     10 T1    2      
##  6     14 T2    2      
##  7     13 T3    2      
##  8     12 T4    2      
##  9      7 T1    3      
## 10     14 T2    3      
## # … with 26 more rows

• Can you say something about within-subject variability (note ‘Minster Blue’)?
• Can you say something about between-subject variability (note ‘Mister Green’, vs ‘Mister Black’)?
• Which chair type takes, on average the biggest effort to arise from?

1. Which chair type takes, on average the biggest effort to arise from? (ANOVA / MEM, fixed effects)

• Do individual (within subject) differences play a role in appointing a average score to a chair type? (MEM, random effects)
• Does variability between subjects play a role in determining the ‘best’ chair type (ANOVA / MEM, confidence intervals)

Statistical models (in R) can be specified by a model formula. The left side of the formula is the dependent variable, the right side are the ‘predictors’. Here we include a fixed and a random term to the model (as is common for mixed-effects models)

library(nlme)

ergo_model <- lme(
  data = ergoStool, # the data to be used for the model
  fixed = effort ~ Type, # the dependent and fixed effects variables
  random = ~1 | Subject # random intercepts for Subject variable
)

The lme() function is part of the {nlme} package for mixed effects modelling in R

Example reproduced from: Pinheiro and Bates, 2000, Mixed-Effects Models in S and S-PLUS, Springer, New York.

plot(ergo_model) ## type = 'pearson' (standardized residuals)

odz: Practice what you preach

If you want to reproduce, add-on, falsify or apply your own ideas to this example, you can find the code (and data) in Github.com

In this course, I will show you how to run, use and organize code like this!